EOPF-Sample-Service/eopf-sample-notebooks
Jun 2025 – Jun 2025
1 Month active
Languages Used
Python
Technical Skills
DaskData AnalysisGeospatial AnalysisPythonSatellite Data ProcessingXarray
Yuvraj198920
PROFILE
Yuvraj198920
Developed and delivered a heatwave detection feature for the EOPF-Sample-Service/eopf-sample-notebooks repository, enabling automated identification of heatwave events using Sentinel-3 Land Surface Temperature data. The solution processes satellite data by filtering for specific date ranges and geographic areas, computes daily maximum LST values, and applies a rolling window analysis to detect sustained periods of high temperatures. Leveraging Python, Dask, and Xarray, the implementation supports integration with analytics dashboards and reporting pipelines, facilitating automated heat risk monitoring. This work accelerates resource planning and response by providing a robust, end-to-end geospatial data analysis workflow tailored for environmental monitoring needs.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
11,931
Activity Months1
Your Network
13 peopleSame Organization
@eurac.edu1
Work History
June 2025
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for EOPF-Sample-Service/eopf-sample-notebooks: Key feature delivered is heatwave detection using Sentinel-3 Land Surface Temperature (LST) data, enabling automated identification of heatwave events within specified date ranges and geographic areas. No major bugs were reported/fixed this month. Overall impact: accelerates heat risk assessment and supports proactive decision-making for resource planning and response. Technologies and skills demonstrated include satellite data processing, daily maximum LST calculation, rolling window analysis for sustained high-temperature detection, and readiness for integration with analytics dashboards and reporting pipelines.
1 Commits • 1 Features
Jun 1, 2025June 2025 monthly summary for EOPF-Sample-Service/eopf-sample-notebooks: Key feature delivered is heatwave detection using Sentinel-3 Land Surface Temperature (LST) data, enabling automated identification of heatwave events within specified date ranges and geographic areas. No major bugs were reported/fixed this month. Overall impact: accelerates heat risk assessment and supports proactive decision-making for resource planning and response. Technologies and skills demonstrated include satellite data processing, daily maximum LST calculation, rolling window analysis for sustained high-temperature detection, and readiness for integration with analytics dashboards and reporting pipelines.
June 2025
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
DaskData AnalysisGeospatial AnalysisPythonSatellite Data ProcessingXarray
Repositories Contributed To
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